A dual-layer flat panel x-ray detector based on an engineered amorphous chalcogenide alloy for quantifying coronary artery calcium

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PROJECT ABSTRACT Heart disease is extremely prevalent, with about one in every four deaths (in the US) being attributed to heart disease. Early detection of cardiovascular events, especially before patients become symptomatic, has immense impact in preventive healthcare, reducing the morbidity and mortality associated with cardiovascular disease. Coronary artery calcification (CAC), a strong predictor for future cardiovascular events, is a component of atherosclerotic plaque buildup in the arteries that supply blood to the heart, leading to coronary artery disease (CAD). Identification of CAC is clinically important because it is used for cardiovascular risk and therapy decision making. Currently, CAC is quantified by computed tomography (CT), however, CT-based population screening is not widely utilized due to cost and radiation burden. Chest x-rays (CXR) are the most common medical imaging procedure and have higher availability than CT in low-resource settings, lower radiation dose, and higher patient throughput that could be used for screening purposes. Unfortunately, due to the lack of quantification in CXR, only qualitative descriptors are possible. The objective of this proposal is therefore to bring much-needed quantification to CXR, particularly for detecting and quantifying CAC by combining a new dual-layer x-ray detector and artificial-intelligence based image processing. The proposed dual-layer detector utilizes alloys of amorphous selenium (a-Se) that achieve favorable electro-optical properties (e.g., higher charge carrier mobilities and higher gain) compared to conventional a-Se based x-ray detectors. This technology has four major components: (1) a top layer direct convection a-Se alloys on an imaging backplane, (2) a bottom layer indirect conversion a-Se alloy with intrinsic gain on an imaging backplane coupled to a scintillator, (3) top panel and bottom panel integration into a dual-layer detector, and (4) a machine learning algorithm that enhances accuracy of the quantitative information from the dual-layer detector. The detector development leverages a mature platform from Varex Imaging, a leading manufacturer of x-ray detectors. We expect to show that the proposed system has higher spatial resolution images and higher sensitivity to detect small, high contrast features (calcifications) and to separate materials such as calcium from soft tissue. This approach will allow accurate quantification of predictive factors and will have immense impact in proactive healthcare, improving the clinical outcomes of patients, and reducing the number of deaths associated with cardiovascular disease. While our focus is on CAC, we expect this technology to broadly improve CXR for early detection of lung cancer, tuberculosis, and other diseases such as osteoporosis via quantification of bone mineral density.